Application of kernel ridge regression in predicting neutron-capture reaction cross-sections. (1st September 2022)

• Record Type: Journal Article
• Authors: Huang, T X; Wu, X H; Zhao, P W
• Is Part Of: Communications in theoretical physics. Volume 74:Number 9(2022)
• Publication Date: 2022-09-01
• Subjects: kernel ridge regression -- machine learning -- neutron-capture reaction; Physics -- Periodicals; 530.105
• DOI: 10.1088/1572-9494/ac763b
• Languages: English
• ISSNs: 0253-6102

Abstract

Abstract: This article provides the first application of the machine-learning approach in the study of the cross-sections for neutron-capture reactions with the kernel ridge regression (KRR) approach. It is found that the KRR approach can reduce the root-mean-square (rms) deviation of the relative errors between the experimental data of the Maxwellian-averaged ( n, γ ) cross-sections and the corresponding theoretical predictions from 69.8% to 35.4%. By including the data with different temperatures in the training set, the rms deviation can be further significantly reduced to 2.0%. Moreover, the extrapolation performance of the KRR approach along different temperatures is found to be effective and reliable.